{"abstract":"A census retains sampling variance.","category":"Survey sampling estimators","checks":6,"contract":"Inputs are nonnegative sample variance s2, integers 1<=n<=population. Return variance of SRS without-replacement mean, rounded to eight decimals; s2 is supplied rather than estimated here.","contract_signature":"s2, n, population","evaluation_group":"model-5813c1966010367d","failed_approach":"Applying the correction to standard error instead of variance squares it.","family":"z-survey_sampling-finite-population","id":"FA-12621","implementations":{"attempt":{"sha256":"43a6b574008d30f25a418425de4fabbae333d75e583d68fcbce7190679549d5a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(s2, n, population):\n    return round((1-n/population)**2*s2/n,8)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('half population',solve(8*N,2,4),2*N)\ncheck('census zero uncertainty',solve(8*N,4,4),0)\ncheck('quarter population',solve(8*N,2,8),3*N)\ncheck('zero dispersion',solve(0,2,8),0)\ncheck('one draw modeled variance',solve(4*N,1,2),2*N)\ncheck('three quarters',solve(12*N,3,4),N)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"5ce25f4898cdc153b5f3ed35634dae86d0e01340d6e47a780dea484b163f5510","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(s2, n, population):\n    return round(s2/n,8)\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('half population',solve(8*N,2,4),2*N)\ncheck('census zero uncertainty',solve(8*N,4,4),0)\ncheck('quarter population',solve(8*N,2,8),3*N)\ncheck('zero dispersion',solve(0,2,8),0)\ncheck('one draw modeled variance',solve(4*N,1,2),2*N)\ncheck('three quarters',solve(12*N,3,4),N)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"Controlled finite fixtures; not a general survey-analysis package. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"z-survey_sampling-finite-population","generated_at":"2026-09-29T14:38:58.620480+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A deterministic survey-design model isolates this sampling contract before it is embedded in a larger estimation pipeline.","root_cause":"The without-replacement finite population correction is omitted.","sha256":"c89d6e741f6db0e794cbac998c0b49396ad145ee7ec5b511c5d8d69e71f0c106","title":"A census retains sampling variance · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":40.695,"exit_code":1,"observations":[{"actual":1.0,"check":"half population","expected":2,"passed":false},{"actual":0.0,"check":"census zero uncertainty","expected":0,"passed":true},{"actual":2.25,"check":"quarter population","expected":3,"passed":false},{"actual":0.0,"check":"zero dispersion","expected":0,"passed":true},{"actual":1.0,"check":"one draw modeled variance","expected":2,"passed":false},{"actual":0.25,"check":"three quarters","expected":1,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"half population\", \"actual\": 1.0, \"expected\": 2, \"passed\": false}, {\"check\": \"census zero uncertainty\", \"actual\": 0.0, \"expected\": 0, \"passed\": true}, {\"check\": \"quarter population\", \"actual\": 2.25, \"expected\": 3, \"passed\": false}, {\"check\": \"zero dispersion\", \"actual\": 0.0, \"expected\": 0, \"passed\": true}, {\"check\": \"one draw modeled variance\", \"actual\": 1.0, \"expected\": 2, \"passed\": false}, {\"check\": \"three quarters\", \"actual\": 0.25, \"expected\": 1, \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":42.609,"exit_code":1,"observations":[{"actual":4.0,"check":"half population","expected":2,"passed":false},{"actual":2.0,"check":"census zero uncertainty","expected":0,"passed":false},{"actual":4.0,"check":"quarter population","expected":3,"passed":false},{"actual":0.0,"check":"zero dispersion","expected":0,"passed":true},{"actual":4.0,"check":"one draw modeled variance","expected":2,"passed":false},{"actual":4.0,"check":"three quarters","expected":1,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"half population\", \"actual\": 4.0, \"expected\": 2, \"passed\": false}, {\"check\": \"census zero uncertainty\", \"actual\": 2.0, \"expected\": 0, \"passed\": false}, {\"check\": \"quarter population\", \"actual\": 4.0, \"expected\": 3, \"passed\": false}, {\"check\": \"zero dispersion\", \"actual\": 0.0, \"expected\": 0, \"passed\": true}, {\"check\": \"one draw modeled variance\", \"actual\": 4.0, \"expected\": 2, \"passed\": false}, {\"check\": \"three quarters\", \"actual\": 4.0, \"expected\": 1, \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}